Intelligent Enterprise: Rebuilding the COO/CIO/CTO Function in the Age of AI

Intelligent Enterprise: Rebuilding the COO/CIO/CTO Function in the Age of AI

Executive Summary

The rise of Generative and Agentic AI presents a historic opportunity for organisations to rethink their operating models. After decades of offshoring business processes and IT operations to low-cost locations, companies can now leverage AI to reshore critical functions, reclaim intellectual property, and create strategic competitive advantages. This paper examines how forward-thinking leaders can transform their operations through an "Intelligent Enterprise" approach that is data-enabled and AI-powered.

·?????? Intelligent EnterpriseTM.? An organisation that uses insight, analytics and artificial intelligence to dynamically reconfigure itself in response to the expected needs of its customers, and simultaneously anticipate and respond to changes and events in the external environment?

In particular, we will focus on what is often the largest function in many organisations, the COO (Chief Operations Function)

The COO Function and the Hidden Scale of Operations

In large companies and public sector organizations, the Chief Operating Officer function represents their largest operational division, typically encompassing:

  • Information Systems
  • Technology Infrastructure
  • Data Management
  • Business Process Operations (formerly "Back Office" functions)
  • Customer Services/Contact Centres
  • Risk and Fraud Management

The true scale of these operations is often obscured, as typically 50% or more of the actual workforce is provided by third-party IT Outsourcing (ITO) and Business Process Outsourcing (BPO) partners, with work predominantly offshored to India, the Philippines, and other low-cost locations.

Most critically, CEOs often fail to recognize that vast amounts of their organisation's intellectual property is embedded not in their "front office" but within these outsourced operational systems and processes.

The Strategic Opportunity

"The question for the CEO now is not, 'What will AI do to us?' BUT 'What will we choose to do with AI?'" — Mark Stouse, 2025

The maturity of Generative and Agentic AI technologies offers a transformative opportunity to reshore operations that were offshored over the past 25 years. Organizations that embrace the "Intelligent Enterprise" model can build data-enabled, AI-powered operations that create sustained competitive advantage.

The critical strategic decision facing CEOs and COOs is whether to:

  1. Allow their ITO/BPO partners to implement AI transformations
  2. Lead this transformation internally to bring core intellectual property and delivery capabilities back onshore

The Offshoring Context

The offshoring of technology and business process work to India has been a dominant trend for UK and US companies over the past 25 years, driven by cost efficiencies:

  • India's IT-BPM sector reached approximately $245 billion in FY 2023, with exports accounting for $194 billion (NASSCOM)
  • India employs over 5 million professionals in the IT-BPM sector
  • The UK IT Outsourcing market was valued at approximately £25-30 billion in 2023-2024
  • The UK Business Process Outsourcing sector was valued at approximately £15-18 billion during the same period

India became the preferred offshoring destination due to:

  • Significantly lower labor costs compared to the UK and US
  • Large pool of skilled technology and business process professionals
  • Established IT infrastructure and mature outsourcing ecosystem

The AI-Driven Transformation

Most of the excitement of AI over the past 2-3 years has been around Generative AI, but AI is not new and comes in a range of capabilities

1.???? Machine Learning is a branch of AI that enables systems to learn from data and make decisions or predictions withot explicit programming. Key Models :

o?? Supervised Learning: Uses labelled data to train models for classification or regression.

o?? Unsupervised Learning: Finds patterns in unlabeled data.

o?? Reinforcement Learning: Models learn by interacting with an environment and receiving rewards.

2.???? Deep Learning (DL) Deep Learning is a subset of Machine Learning that utilizes neural networks with multiple layers (deep neural networks) to learn complex patterns.

o?? Artificial Neural Networks (ANNs): Multi-layer perceptrons that process structured and unstructured data.

o?? Convolutional Neural Networks (CNNs): Specialized in analyzing visual data for image classification, facial recognition, etc.

o?? Recurrent Neural Networks (RNNs): Used for sequential data processing (e.g., speech recognition, time-series prediction).

o?? Transformers: Advanced neural architectures (e.g., BERT, GPT) that improve performance on NLP tasks.

3.???? Agentic AI refers to AI systems capable of autonomous decision-making and problem-solving without direct human intervention. These AI agents can perform multi-step tasks, plan strategies, and adapt dynamically to new environments.

o?? Reinforcement Learning Agents: Learn through trial and error in simulated or real-world environments.

o?? Multi-Agent Systems: Networks of AI agents that collaborate or compete to achieve objectives.

o?? Large Language Model (LLM)-Based Agents: Use foundation models like GPT-4,

4.???? Generative AI refers to models that generate new data, such as text, images, audio, or even synthetic data, by learning patterns from existing datasets.

o?? Generative Adversarial Networks (GANs): Composed of a generator and discriminator network that create realistic outputs.

o?? Variational Autoencoders (VAEs): Probabilistic models used to generate new data points resembling training data.

o?? Transformers (LLMs): Large-scale models that generate human-like text.

These categories of AI ?often overlap and integrate:

·?????? Deep Learning provides the foundation for modern NLP and Generative AI

·?????? Generative AI often uses sophisticated Deep Learning architectures

·?????? Agentic AI frequently incorporates Generative AI for reasoning and NLP for interaction

·?????? Machine Learning principles underpin all other categories

Quantifying the Automation Potential

These AI technologies are poised to dramatically impact offshore operations:

  • Gartner predicts that by 2025, 70% of organizations will implement structured automation architectures, up from 20% in 2021
  • McKinsey estimates that 44% of core IT activities could be automated with existing technologies
  • The World Economic Forum's Future of Jobs Report 2023 indicates 42% of task hours will be completed by machines by 2027

Based on industry analyses and pilot projects, the automation potential across key functions includes:

IT Outsourcing Automation Potential:

  • Infrastructure Management: 45-55% of tasks
  • Application Management: 30-40% of tasks
  • Service Desk/Help Desk: 50-65% of tasks
  • Software Development: 25-35% of tasks

BPO Automation Potential:

  • Customer Service: 40-55% of tasks
  • Finance & Accounting: 50-60% of tasks
  • HR Services: 35-45% of tasks
  • Procurement: 40-50% of tasks
  • Document Processing: 60-75% of tasks

Overall, approximately 40-50% of current outsourced workloads can be automated using AI technologies, representing approximately £17-24 billion of the combined market value in the UK alone.

The Customer Service Revolution

Customer service warrants special focus, as companies like Octopus Energy have demonstrated that GenAI-powered contact centers can outperform traditional human-staffed operations while delivering superior customer experiences.

This seeming paradox exists because many contact centers have already been "dehumanized" in the pursuit of cost reduction, imposing frustrating delays on customers. GenAI can provide near-instantaneous responses and seamlessly escalate to human agents when necessary, creating a more satisfying customer journey.

The Software Development Transformation

Software development is experiencing particularly dramatic disruption:

  • Salesforce has announced they are no longer hiring software engineers, as AI capabilities are augmenting existing teams
  • Anthropic CEO Dario Amodei predicts 90% of all code will be written by AI within 12 months

While software development won't disappear overnight, the nature of the work is shifting. Human expertise will remain essential for design, architecture, and change management—the higher-value activities that directly contribute to intellectual property. This shift creates the opportunity to reduce dependence on offshore providers while maintaining high-skill, high-value roles onshore.

Implications for the COO Function

The COO function represents the critical operational engine of every business. The massive outsourcing trend has created significant IP leakage and dependency risks that many organizations now have the opportunity to address.

For CEOs and COOs, the strategic questions are:

  1. How will we leverage AI to transform our operations?
  2. What critical roles will humans play in an AI-augmented organization?
  3. Where will those humans be located—in high-cost or low-cost regions?

To maintain high-quality onshore work and the skilled workforce that delivers it, UK and US organizations must reconsider their dependence on offshore partners.

The Path Forward: Building the Intelligent Enterprise

Organizations that embrace the Intelligent Enterprise model will develop "data-enabled, AI-powered" operations that build capabilities for "change as usual." By leveraging Generative and Agentic AI with work orchestrated onshore rather than offshore, they can reduce external dependencies while maximizing competitive advantage.

The critical question for leaders:

Will you seize this opportunity to redesign your operations function, making it fully data-enabled and AI-powered—transforming it into a strategic value creator for your business?

Sources

  • NASSCOM
  • ISG Index (Information Services Group)
  • KPMG UK IT Outsourcing Survey
  • Gartner Market Share Analysis reports
  • Technavio Market Research
  • GlobalData UK IT Services Market reports
  • UK Office for National Statistics

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Shaun Taylor

CIO | COO | CTrO | NED | Driving Transformation & Operational Performance Through Proven Experience | Private Equity - Integration, & Value Creation | Transformation Recovery | London & Barcelona-Based (Schengen Ready)

5 天前

Having worked extensively in BPO and ERP focused transformation, I see generative AI as a paradigm shift that could reverse the outsourcing trend. BPO has traditionally been driven by labor cost arbitrage, but as AI reduces labor dependency, the business case for BPO weakens. I can now see the business case swinging back in favour of insourcing, as AI-driven efficiency erodes the cost advantage of offshoring. This shift will require businesses to do more than rethink operating models, it will mean a greater focus on accuracy and improved MDM practices and for many that will be a more challenging journey than insourcing.

Bill Schmarzo

Dean of Big Data, CDO Chief AI Officer Whisperer, recognized global innovator, educator, and practitioner in Big Data, Data Science, & Design Thinking

6 天前

Eddie, this is a very interesting article, and it highlights the challenge that organizations face in establishing the appropriate leadership structure to ensure that they are getting the most value out of their AI and data initiatives. The old roles just don't work well in this new age of value creation through AI and data. The CIO and CTO are not equipped to handle that role as their background and expertise lie in technology. For the most part, they really don't understand the business and probably don't need to understand the business well given their challenges of staying on top of technology changes and ensuring the the technology lights stay on. I wrote in the blog below about the growing importance of the CDO (and Chief Economist) role because it is less about technology and more about economics (and understanding the organization's sources of customer, operational, and societal value creation). I had not considered the COO, but I agree with your assessment that they are the masters of scaling value creation. "Digital Age Kool Kids Klub:? Chief Data Officers and Chief Economists" https://www.datasciencecentral.com/digital-age-kool-kids-klub-chief-data-officers-and-chief-economists/

Matt Austin

Consulting Leader | Data & Analytics | Sales & Business Development

1 周

Nice article Eddie Short - thanks. One observation I have over the last year or two with clients, is that the COO function can have a fairly risk-averse stance in a many enterprises. I wonder if this stance is a blocker to innovation (e.g. risk of deploying agents - what if they say the wrong thing to the customer, risk of Gen AI - what if we inadvertently let loose proprietary information, etc.). Do you have any advice for COOs in this position?

Mike Weston-Burt

Freelance trainer | eduTainer | AI/Web3 Ninja | ex-Big4 | Innovation Mgr & Creativity Magician. Helping Cultivate Creative Minds across all levels and sectors.

1 周

Very helpful

Dan French

CEO at Consider Solutions

1 周

"Think not what you can do for AI, but what AI can do for you". ?? But only after you have worked out what business opportunities and challenges you actually have! You dont want a vAIcuum ! (AI in a vacuum)

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